Now, the United Kingdom's Ministry of Defence (MoD) has held its own robotics competition, the Grand Challenge, that cut to the chase with unmanned vehicles stalking human targets through the Copehill Down training village in southwestern England. The finals took place this weekend, and the MoD announced the winners yesterday.

Advertisement - Continue Reading Below

A key difference between the Grand Challenge and DARPA's Challenges is hardware diversity. The robots who slogged through the training village, picking out an array of potential targets--including uniformed troops, armed snipers perched in windows and roadside bombs--ranged from familiar, sensor-studded unmanned ground vehicles (UGVs) to swarms of unmanned aerial vehicles (UAVs). Some teams even used a combination of ground and air bots, since UAVs might be useful for spotting a tactical (a pickup with a mounted weapon) while UGVs are better at detecting improvised bombs. Less "operator intervention" required to navigate the village, find warm bodies and differentiate between civilians and legitimate military targets earned more points.

More From Popular Mechanics

Stellar won the competition with its SATURN system, and PM picked three other teams that took innovative approaches to the challenge. The winner won't receive a cash prize, so the entire competition was essentially an open audition. The most impressive systems could land contracts with the MoD or be snapped up by larger defense firms, even if they didn't take home the trophy.

Stellar

The Stellar team's Grand Challenge entry, SATURN (short for Sensing & Autonomous Tactical Urban Reconnaissance Network), took the top prize in yesterday's MoD competition: Of the entries, it was the most successful at identifying threats and relaying them back to the team. This doesn't mean that Stellar will automatically get a contract with the British Armed Forces, but the MoD will consider it for incorporation into its future frontline kit, along with successful elements from other teams' entries.

In many ways SATURN is something out of a sci-fi nightmare: Swarms of mindless drones buzz through densely populated cities and towns and report back to a centralized computer that analyzes the situation and feeds back commands. In the case of Team Stellar, its Ground Control Station (GCS) consists of a laptop that processes image data from a combination of unmanned ground and air vehicles.

According to Julia Richardson, director of Stellar Research Services and head of the multi-organization team, the GCS hosts two software suites. The Autonomous Threat Detection software looks at incoming footage from the single UGV and multiple UAVs and picks out threats. It then indicates the location of each threat on an overhead map, which is displayed on the laptop screen.

"Meanwhile, the IP [Intelligent Processing] software decides which platforms should be sent where, and then provides them with the commands and routes, via GCS waypoints," says Richardson. Instead of relying on GPS data, Stellar's hivemind-like SATURN system plans routes based on aerial imagery, collected in advance by satellites or UAVs.

Stellar's UAVs may not be in the British army's immediate future, but their success in the Grand Challenge means that the technology is hardly science fiction.

Mindsheet

If this team had its way, tomorrow's soldiers wouldn't be able to walk past a Toys R Us without breaking into a sweat. The fleet of four UGVs that Mindsheet deployed were made from off-the-shelf radio-controlled toy cars fitted with off-the-shelf sensors and communications gear.

"This is an awesome vehicle," says Raglan Tribe, managing director of Mindsheet Ltd. The Traxxas EMAXX can power through wet terrain and over curbs, and has a maximum speed of 30 mph. "You can drop it from about 2 meters without damaging it," says Tribe. "We haven't wasted any time or resources reengineering the chassis. The same applies to the vehicle controller, the sensors, communications, etc. Instead, we are putting our effort into threat detection algorithms and vehicle control behaviors."

Using commercial gear could streamline maintenance, but if the backpack-portable drones ever make it to the battlefield, their existing battery life would be boosted. Mindsheet's UGVs navigate the training village with full autonomy, but once potential targets are spotted, classifying each threat requires some human assistance. Before the competition Tribe noted, "We might lose a few points, but it's probably the only way to pick out the IED and sniper."

Advertisement - Continue Reading Below

MIRA

Team Mira consists of a trio of robotic scouts that together seem more like a traveling robot circus act than a team of military droids. Its entry consists of a UGV called MACE 1, which carries a small UAV on its back, and tows a trailer holding a small blimp. MACE 1 (which stands for MIRA Autonomous Control Engineering) is clearly the ringleader, a hybrid-electric four-wheeled robot that has autonomous navigation and threat-detection software. The flying saucer-like UAV that comes off of MACE 1's back has to be controlled by a human operator, while the blimp's job is simply to relay images from high above the battlefield.

The star of this team--which, like Stellar, includes both private and academic organizations--is MACE 1, with its ability to turn off its internal combustion engine for stealth missions and haul payloads as heavy as 660 pounds. And while Team Mira's blimp and remote-operated flying saucer could help locate threats, most of the sensor capabilities are mounted on the UGV. Though the three-part combo didn't win the trophy, the competition could serve as a launching pad for this versatile drone.

Silicon Valley

If Team Mira's robots are a circus act, then Silicon Valley's fleet of drones might be an entire circus. This team's system is designed to be easily scaled up or down and can include different kinds of UGVs, static sensors (dropped in place by UGVs), various UAVs, tethered blimps and kites.

At the MoD Grand Challenge, the team's robot army featured a pair of six-wheeled UGVs called Moonbuggies. There are two versions of the Moonbuggy: a larger model, designed to investigate the environment, and a mini-Moonbuggy that generally stays in one spot or patrols a given area. The system also includes a glider that can fly on autopilot over the village, feeding video to a pair of glasses. Similar to Team Mindsheet's easily replaceable toy cars, Silicon Valley's system is intended to use largely off-the-shelf, commercially available gear.

Though its robots are impressive, the algorithm that the system uses is perhaps its most striking asset. It includes image recognition software that can--in theory--spot the command wires leading back to an improvised explosive device. Though the Silicon Valley team didn't take first place in the Grand Challenge, the team's technical lead, Richard May, believes they could have "a trialable system in six to nine months and a fieldable system within 18 months."